Bringing a GAN to a Knife-Fight: Adapting Malware Communication to Avoid Detection

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F18%3A00322702" target="_blank" >RIV/68407700:21230/18:00322702 - isvavai.cz</a>

Result on the web

<a href="https://ieeexplore.ieee.org/document/8424635/?part=1" target="_blank" >https://ieeexplore.ieee.org/document/8424635/?part=1</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/SPW.2018.00019" target="_blank" >10.1109/SPW.2018.00019</a>

Result language

angličtina

Original language name

Bringing a GAN to a Knife-Fight: Adapting Malware Communication to Avoid Detection

Original language description

Generative Adversarial Networks (GANs) have been successfully used in a large number of domains. This paper proposes the use of GANs for generating network traffic in order to mimic other types of traffic. In particular, our method modifies the network behavior of a real malware in order to mimic the traffic of a legitimate application, and therefore avoid detection. By modifying the source code of a malware to receive parameters from a GAN, it was possible to adapt the behavior of its Command and Control (C2) channel to mimic the behavior of Facebook chat network traffic. In this way, it was possible to avoid the detection of new-generation Intrusion Prevention Systems that use machine learning and behavioral characteristics. A real-life scenario was successfully implemented using the Stratosphere behavioral IPS in a router, while the malware and the GAN were deployed in the local network of our laboratory, and the C2 server was deployed in the cloud. Results show that a GAN can successfully modify the traffic of a malware to make it undetectable. The modified malware also tested if it was being blocked and used this information as a feedback to the GAN. This work envisions the possibility of self-adapting malware and self-adapting IPS.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/TH02010990" target="_blank" >TH02010990: Ludus: Machine Learning and Game Theory to Collaboratively Defend Against Internet Threats</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2018

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Proceedings of 2018 IEEE Symposium on Security and Privacy Workshops

ISBN

978-1-5386-8276-0

ISSN

—

e-ISSN

—

Number of pages

6

Pages from-to

70-75

Publisher name

IEEE Computer Society

Place of publication

USA

Event location

San Francisco

Event date

May 24, 2018

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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